The document proposes a new method for training language models. The method is based on the idea of self-supervised learning, where the model is trained on a large corpus of unlabeled data. The model is then fine-tuned on a downstream task, such as natural language inference or question answering. The authors show that their method achieves state-of-the-art results on a variety of tasks.
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